Real-time voltage stability assessment (VSA) has long been an extensively research topic. In recent years, rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data. Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states, or even unstable state. However, in practice, the data of insecure or unstable state is very rare, as the power system should be guaranteed to operate far away from voltage collapse. Under this circumstance, this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system. The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data, while it fails to rebuild data of insecure states. Thus, the residual of reconstruction is effective in indicating VSA. Besides, to develop a more accurate and robust algorithm, long short-term memory (LSTM) networks combined with fully-connected (FC) layers are used to build the autoencoder, and a moving strategy is introduced to bias the features of testing data toward the secure feature domain. Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.

中文翻译：

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使用自动编码器的基于重构残差的长期电压稳定性评估

实时电压稳定性评估（VSA）一直是广泛的研究主题。近年来，快速安装的深度学习方法将在线VSA推向了一个新高度，即从测量数据的角度来看，大量学习算法已应用于VSA。深度学习方法通​​常需要包含安全状态和非安全状态甚至不稳定状态下的测量值的大型数据集。但是，实际上，不安全或不稳定状态的数据非常少见，因为应确保电源系统在远离电压崩溃的地方工作。在这种情况下，本文提出了一种基于自动编码器的方法，该方法仅需要安全状态数据即可评估电力系统的电压稳定性。此方法的原理是，仅由安全数据训练的自动编码器将只能为安全数据创建精确的重建，而无法重建不安全状态的数据。因此，重构的余量在指示VSA方面是有效的。此外，为了开发更准确，更健壮的算法，将长短期内存（LSTM）网络与全连接（FC）层相结合来构建自动编码器，并引入了一种移动策略来将测试数据的特性偏向于安全功能域。大量实验和与传统机器学习算法的比较证明了该方法的有效性和高精度。重建的残差可以有效地指示VSA。此外，为了开发更准确，更健壮的算法，将长短期内存（LSTM）网络与全连接（FC）层相结合来构建自动编码器，并引入了一种移动策略来将测试数据的特性偏向于安全功能域。大量实验和与传统机器学习算法的比较证明了该方法的有效性和高精度。重建的残差可以有效地指示VSA。此外，为了开发更准确，更健壮的算法，将长短期内存（LSTM）网络与全连接（FC）层相结合来构建自动编码器，并引入了一种移动策略来将测试数据的特性偏向于安全功能域。大量实验和与传统机器学习算法的比较证明了该方法的有效性和高精度。